Full Stack AI Engineer

Full Stack AI Engineer

Work Location & Reporting Address: New York, NY/Pittsburgh, PA/Lake Mary, FL (Hybrid (2-4 days' a week work from office) Contract duration: 12 months Does this position require Visa independent candidates only? Yes Interview Process (Is face to face required?) Video mode Minimum years of experience required: 5+ years of relevant experience; 10+ years of overall experience Must Have Skills: AI, Python Nice to Have Skills: Java Springboot, Angular Detailed Job Description:

1. Design and develop AI/ML solutions using supervised, unsupervised, deep learning, NLP, time series forecasting, and anomaly detection techniques to address business challenges.

2. Build Generative AI applications leveraging LLMs, prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), and AI agent frameworks.

1. Experience of working for banking domain 2. Develop and maintain end-to-end AI pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.

3. Demonstrate strong programming expertise in Python and SQL, with hands-on experience in ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and NLTK.

4. Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric.

5. Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.

6. Deploy AI models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.

7. Utilize cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.

8. Collaborate with business and technology stakeholders to translate business requirements into scalable AI solutions while tracking ROI and value realization.

9. Contribute to AI innovation and best practices through reusable frameworks, AI copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques